Barr Moses

Deploying agents at scale is a major endeavor for any organization; in healthcare, the stakes are especially high. Unpredictable behaviors & unreliable outputs pose a risk to patients, invite scrutiny from regulators, & erode hard-won institutional trust.

The enterprises with the most to gain from AI are often the slowest to deploy it, and in regulated industries, that caution is rational — the risk is real. Data quality failures don't just break pipelines in financial services — they break compliance, erode model trust, and end careers. The bar for "production-ready" AI is fundamentally different when your data touches trades, audits, or PII. In this session, Barr Moses, CEO of Monte Carlo, joins Lenny Rosenfeld, VP of Data Science & Software Engineering at Nasdaq, for a conversation on what it takes to get trusted AI into production in financial services. They'll cover AI observability, data lineage, and governance frameworks built for regulators — sharing the failures they've seen, the patterns that work, and what they'd do differently.

Every company is shipping AI agents right now. Most of them are failing, and nobody knows until a customer or an exec calls it out. The problem isn't the models. It's that teams are building agents on top of data they don't monitor, running them without observability and trusting outputs no one has tested. In this session, I'll walk through the three places agents actually break in production: the data going in, the agent's own behavior and what comes out the other side. I'll share a framework for catching these failures before your users do. The goal is simple: stop shipping pilots that quietly die, and start shipping agents that hold up when it matters.

In 2026, data and AI leaders will be measured by the success of AI initiatives. So how can teams move beyond failing pilots and toward reliable, production-grade AI at scale? In this session, Barr Moses, CEO and Co-Founder of Monte Carlo, will explore how enterprise teams should monitor the most critical components of agents — inputs, operational metrics and outputs — to ensure they’re trusted and well adopted. Attendees will learn best practices for operationalizing data and AI observability and leveraging Snowflake as a critical nexus to close the loop between data inputs and AI outputs.

As organizations scale AI and real-time analytics, ensuring trusted, high-quality data is a business-critical priority. In this panel, Vinay Gupta, VP and GM of Data Platform Services at Salesforce, Philip Zelitchenko, VP of Data Science and AI Platform at Dropbox, and Nithin Ramachandran, Global VP of Data Analytics, MDM and AI at 3M, share how they're modernizing their data and AI platforms to deliver reliable data with and end-to-end observability at enterprise scale. From building data meshes and semantic layers to monitoring models and metrics, they discuss how leading teams are shifting to proactive reliability and bulding the architecture for the next generation of AI products.

As more organizations feel the pressure to deploy AI in production, their leaders face a critical challenge: moving faster without sacrificing reliability. This panel brings together data + AI leaders to discuss how a proactive approach to data foundations empowers their teams to deliver reliable AI at scale. This discussion will explore the relationship between strategy, structure, and tooling to drive business impact and work smarter.

In 2026, data + AI leaders will be measured by the success of AI initiatives. So, how can teams move beyond failing pilots and into reliable, production-grade AI? This session will explore how to monitor critical components of Agents—inputs, operational metrics, and outputs—to ensure they’re trusted and well-adopted. Attendees will learn best practices for leveraging data + AI observability to close the loop between data inputs and AI outputs.

Agentic AI is the next evolution in artificial intelligence, with the potential to revolutionize the industry. However, its potential is matched only by its risk: without high-quality, trustworthy data, agentic AI can be exponentially dangerous. Join Barr Moses, CEO and Co-Founder of Monte Carlo, to explore how to leverage Databricks' powerful platform to ensure your agentic AI initiatives are underpinned by reliable, high-quality data. Barr will share: How data quality impacts agentic AI performance at every stage of the pipeline Strategies for implementing data observability to detect and resolve data issues in real-time Best practices for building robust, error-resilient agentic AI models on Databricks. Real-world examples of businesses harnessing Databricks' scalability and Monte Carlo’s observability to drive trustworthy AI outcomes Learn how your organization can deliver more reliable agentic AI and turn the promise of autonomous intelligence into a strategic advantage.Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

Agentic success starts with trustworthy data. In this session, Monte Carlo CEO and Co-Founder Barr Moses will explore how companies can accelerate AI readiness through data + AI observability. Learn why reliable, high-quality data is the foundation for effective AI initiatives — and how first-party data is your greatest competitive boat in the AI race. Barr will share strategies for ensuring data reliability, minimizing risk and scaling AI projects with confidence, drawing on real-world examples from leading data-driven organizations.

Hear expert data leaders from top financial services firms share insights on navigating the opportunities and challenges of becoming AI-ready with data + AI observability. They'll discuss how they modernize data strategies, tackle governance and compliance, and leverage Snowflake’s capabilities to unlock AI-driven insights. Gain practical approaches to data democratization, risk management and accelerating AI adoption in the highly regulated world of financial services.

DIRECTV, one of the largest content distributors in the U.S., didn’t just modernize their data stack — they reimagined it. By implementing a data mesh and rearchitecting their data landscape from the ground up, they established a single source of truth to drive measurable business impact, while reducing their footprint and optimizing their costs. To ensure this new system delivered trusted, reliable data at scale, they turned to data + AI observability with Monte Carlo to drive data quality, increase stakeholder confidence, and lay the foundation for AI innovation.

Generative AI observability holds the potential to transform enterprise data teams. In this session, deep dive into the architecture, benefits and challenges of T. Rowe Price’s gen AI use cases for data discovery and data supply chain innovation — resulting in 50% increased efficiency. Learn how the key components of gen AI observability, including observability into unstructured data, vector databases, LLMs and the AI data stack accelerate data quality management.

Broken data is costly, time-consuming, and nowadays, an all-too-common reality for even the most advanced data teams. In this talk, I’ll introduce this problem, called “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and discuss how to eliminate it in your data ecosystem with end-to-end data observability.

A shared understanding of how data is defined and managed provides confidence in how it's used. Metadata makes data valuable. But it also holds enormous potential for creating value in and of itself, through workflow tuning, optimization, and a variety of other performance-based use cases.